They range from growth on the leads of cardiac pacemakers, thorough biofilm attached to the inner surface of water distribution pipes, to the epilimnion of rocks in streams and accumulated plaque on the surface of teeth.

During biofilm development, a large number of phenomena occur simultaneously and interact over a wide range of length and time scales. As a result of nutrient conversions, the biofilm expands on the basis of bacterial growth and production of extracellular polymeric substances (EPS). Chemical species need to be ontinuously transported to and from the biofilm system by physical processes such as olecular diffusion and convection. Fluid flow influences biofilm growth by determining the concentrations of available substrates and products. On the other hand, the flow also shears the biofilm surface, and determines biofilm detachment processes. In the case of multi-species systems, microorganisms of different species interact in complex relationships of competition or cooperation. All these linked phenomena create a dynamic picture of the biofilm three-dimensional (3D) structure. The large number of localized interactions poses an important challenge for experimentalists. Mathematical models can prove useful because they allow testing of hypotheses and, in addition, can direct experimental efforts to complex regions of operation that can easily confound the general intuition. Although the word “modeling” is used for different purposes, the final result is invariably the same: models are no more than a simplified representation of reality based on hypotheses and equations used to rationalize observations. By providing a rational environment, models can lead to deeper and more general understanding. Ultimately, understanding the underlying principles becomes refined to such a state that it is possible to make accurate predictions.

The general view on microenvironemt in biofilm has dramatically changed during the last decade. It has been previously assumed that most of the biofilms are more or less homogeneous layers of microorganisms in a slime matrix. The use of Confocal Scanning Laser Microscopy (CSLM), and computerized image analysis tools revealed a more complex picture of biofilm morphology (Lawrence et al., 1991; Caldwell et al., 1993). CSLM and other optical investigations have shown that some biofilms possess a heterogeneous structure (Costerton et al., 1994; Gjaltema et al., 1994). Cell clusters may be separated by interstitial voids and channels, that create a characteristic porous structure. In some particular cases, biofilms grow in the form of microbial clusters taking a "mushroom" shape (Figure 1.1a, b – reproduced from Stoodley et al., 1999b). In other cases, more compact and homogeneous biofilm layers can be observed.

In many biofilms the reported nonuniformaties are other than gradients in only one direction, perpendicular to the substratum. Three-dimensional variation of microbial species, biofilm porosity, substrate concentration or diffusivities has been repeatedly reported. It is becoming clear that there are many forms of heterogeneity in biofilms, and a definition of biofilm heterogeneity is needed. According to Bishop and Rittmann (1995), heterogeneity may be defined as "spatial differences in any parameters we think is important". An adapted list from Bishop and Rittmann (1995), summarizes a few examples of possible biofilm heterogeneity:

同一文章４：黄色でハイライトされた部分が同一文章Activity of microbes in biofilms is often notably different from that observed when they are in the suspended planktonic phase. Microbial cells enclosed in a biofilm matrix show significant advantages in relation to their planktonic counterparts, namely in the resistance to aggressive agents, such as increased resistance to disinfectants and antibiotics (Ceri et al., 2001) and to ultraviolet radiation (Elasri &Miller, 1999), drying (EPS are highly hydrated) and protection from grazing by predators such as protozoa.Conversely, there are also notable disadvantages for bacteria when growing in a biofilm, such as an increased competition for limiting resources and increased mass transfer resistance, interference competition by production of antibiotics, overgrowth, and increased pressure from parasites. Most of such observed characteristics of microbial growth in biofilms can be explained by invoking transport phenomena, i.e. the physical implications of growth in densely packed environments where fluid flow is reduced. In sufficiently thick biomass clusters, as are generally the case in biofilms, diffusional distances are long enough that solute transport to inner bacterial cells becomes slow in comparison with the bioconversion kinetics of the microorganisms. In such situations, solute gradients are formed throughout the biofilm and mass transport becomes the rate-limiting process of the various biotransformations occurring (Characklis et al., 1990). In these environments, solute gradients provide favorable conditions for the creation of functional micro-niches. For example, the depletion of oxygen in proportion to depth observed in activated sludge flocs (Schramm et al., 1999; Meyer et al., 2003) and biofilms (de Beer et al., 1993) can create microenvironments suitable for the proliferation of anaerobic organisms, despite the presence of dissolved oxygen in the surrounding liquid phase. Solute gradients in oral biofilms also account for the local acidity that causes caries. In dental plaque, acidogenic and aciduric (acid-tolerating) bacteria rapidly metabolize dietary sugars to acids, which gradually accumulate, creating acidic microenvironments that are at the same time responsible for enamel demineralization (tooth decay) and for inhibiting competition from species associated with enamel health (Marsh, 2003). Mass transport limitations that impede efficient antibiotic penetration in biofilm matrices are frequently appointed as possible mechanisms responsible for the mentioned resistance to antibiotics (for references, see Mah & O’Toole, 2001). In light of these facts, an interpretation of the biofilm behavior from the extrapolation of the planktonic cell is not possible without knowledge of the mass transfer processes, in this complex morphology, responsible for the creation of the microenvironments (de Beer & Schramm, 1999).

同一文章５：黄色でハイライトされた部分が同一文章1.3.1 Why biofilm modeling?To study the complex interaction between many of the factors acting simultaneously, we need a mathematical model. Besides experimentation, mathematical abstraction of the reality can help understanding interdependence of biofilm processes. The IAWQ International Specialty Conference on Microbial Ecology of Biofilms (Lake Bluff, IL, October 1998) provided updated information on current issues in biofilm research. According to their destination, biofilm models can be broadly classified into two categories:

2. Advanced models used as research tools to investigate specific processes occurring within microbial biofilms. The application of these models is primarily intended to fill gaps in our knowledge of biofilm dynamics.

In relation to practical engineering applications, the current objectives of biofilm modeling include biofilm engineering, real-time control, and applications in education. These objectives are briefly described in the following section:

1. Biofilm Engineering. If more insight is gained on the interactions between processes involved in biofilm formation, then it would be possible to “engineer” the biofilm structure and its function. For example, we may envision the manipulation of the environmental conditions to generate dense biofilm structures that will be easily separated from a liquid phase (e.g., granules in UASB reactors, in fluidized bed or airlift reactors), or rough biofilm structures with high capacity for removal of particulate material.

2. Real-Time Control. The ability to control biofilm systems on-line requires mathematical models that incorporate both the activity of the biofilms and the stochastic behavior of system inputs.

3. Education. Biofilm models are also learning tools. If mathematical models of biofilms are to be used as design and simulation tools, it is essential to teach the fundamentals of these models to scientists and engineers. Moreover, a better understanding of basic physical and computational principles, as well as of the benefits and limitations of existing models, would contribute to an increased appreciation of the mathematical model as a basic tool for research and practical applications.

The current use of biofilm models as research tools has broader objectives, most of them related to gaining a better understanding of 3-D biofilm structure, of population dynamics, and of mechanical factors affecting biofilm formation:

1. Relevance of 3-D heterogeneity. With the abundant experimental evidence showing that biofilm structures are heterogeneous, the simplifying assumptions of 1-D models are in question. As important as the development of useful models for biofilm engineering is the critical evaluation of these original assumptions. It is fundamental to propose and develop unifying parameters to describe biofilm structure and to investigate trends within the biofilms. It is equally significant to evaluate the importance of biofilm heterogeneity on overall biofilm reactor performance.

2. Microbial Ecology. Novel experimental methods are continuously producing more evidence of the heterogeneous nature of multispecies biofilms. Even though it is possible to develop hypotheses on the ecological interactions among different microorganisms based on the experimental observations, mathematical modeling is a key tool to evaluate the adequacy of the hypotheses.

4. Analysis of potential detachment mechanisms. Advanced mathematical modeling of biofilms can be used to understand the effect of hydrodynamic flow and shear forces on the erosion and sloughing mechanisms in biofilms. These mathematical efforts need to be complemented with experimental information on mechanical properties of biofilms, such as elasticity and tensile resistance as a function of EPS and cell content.

Mathematical models have been used for the last three decades as tools to simulate the behavior of microbial biofilms. The initial models described biofilms as uniform steady-state films containing a single type of organism (Fig. 1.1a), governed exclusively by one-dimensional (1-D) mass transport and biochemical transformations (Atkinson and Davies, 1974; Rittmann and McCarty, 1980). Later, stratified dynamic models (Fig. 1.1b) able to represent multisubstrate-multispecies biofilms (Wanner and Gujer, 1986; Wanner and Reichert, 1996) were developed. Although these 1-D models were advanced descriptions of multispecies interactions within the biofilm, they were not able to provide the characteristic biofilm morphology. Biofilm morphology is an input in these models, not and output. Structural heterogeneity in biofilms was already known at that time, but it has been recently underlined through numerous experimental observations. New biofilm models are needed now, to provide more complex two- and three-dimensional descriptions of the microbial biofilm (Fig. 1.1c), and incorporate solutes mass transport and transformation, population dynamics and hydrodynamics. This evolution in model complexity has paralleled the advances in computational tools. While hand calculators were the tools used in the 1970's, the biofilm models of today reflect the availability of fast personal computers and advanced parallel processing.

The amount of experimental evidence describing some biofilms as heterogeneous entities in structure and composition contradicts the simplifying assumptions of the original 1-D models. This has challenged engineers to create a more accurate mathematical description of biofilms. The challenge has resulted in an increasing model complexity, derived from the inclusion of an ever increasing number of parameters to explain the biofilm structure. The new generation of structural biofilm models should describe/predict the formation of microcolonies, the development of heterogeneous colonization patterns, the sloughing of large biofilm sections. They could be further expanded to simulate experimentally observed phenomena such as formation of streamers and advective flux through microchannels. Nevertheless, the real challenge to the modeler is not to create models that include as many parameters as possible, but rather, to determine the level of significance of these parameters in the description of the different biofilm processes. Moreover, the mathematical evaluation of parameter significance is essential to define the required level of accuracy of experimental measurements.

Figure 1.1 Evolution of biofilm models from (a) uniform biomass distribution and one-dimensional substrate gradient in the 1970’s, to (b) one-dimensional stratified biomass and multispecies biofilms in the 1980’s, to (c) multidimensional distribution of biomass and substrate at the end of 1990’s.

AQUASIM is a computer program for the identification and simulation of aquatic systems. The program includes a 1-D multisubstrate and multispecies biofilm model and represents a suitable tool for biofilm simulation. The program can be used to calculate substrate removal in biofilm reactors for any user specified microbial systems. 1-D spatial profiles of substrates and microbial species in the biofilm can be predicted. The program also caiculates the development of the biofilm thickness and of the substrates and microbial species in the biofilm and in the bulk fluid over time. Detachment and attachment of microbial cells at the biofilm surface and in the biofilm interior can be considered, and simulations of sloughing events can be performed. Futhermore, AQUASIM allows pseudo 2-D modeling of plug flow biofilm reactors by a series of biofilm reactor compartments. The most significant limitation of the model is that it only considers spatial gradients of substrates and microbial species in the biofilm in the direction perpendicular to the substratum.

1.3.3.1 Features of the biofilm model implemented in AQUASIM

For biofilm modeling and simulation, AQUASIM offers a biofilm reactor compartment consisting of three zones: “bulk fluid,” “biofilm solid matrix,” and “biofilm pore water” (Fig. 1.2). For all three zones, AQUASIM calculates the development over time of microbial species and substrates, as well as the biofilm thickness. In the biofilm, spatial gradients perpendicular to the substratum are calculated for microbial species and substrates. The bulk fluid is assumed to be completely mixed, and a liquid boundary layer between the biofilm and the bulk fluid can be considered. The AQUASIM biofilm reactor compartment can be connected to other compartments. Solid arrows in Fig. 1.2 indicate possible mass fluxes across the compartment boundaries. These fluxes include influent, effluent, exchange between the bulk fluid and the atmosphere, and transport across a permeable substratum.Sharedarrows indicate mass fluxes between the various zones in the compartment. These fluxes account for detachment and attachment of microbial cells in the biofilm and at the biofilm surfaces and diffusion of soluble and suspended particulate compounds through the liquid boundary layer.

In the AQUASIM dialog box “Edit Biofilm Reactor Compartment”, the properties of the biofilm systemtobe modeled are specified. The reactor type is chosen to be “confined” if the volume of the biofilm plus the bulk fluid is constant, as is the case in a closed reactor, and to be “unconfined” if the biofilm can grow freely, as may be the case in a trickling filter. The pore volume can be specified to contain only a liquid phase and dissolved substrate, or it can also contain suspended solids. The biofilm matrix can be assumed to be rigid, i.e., to change its volume due to microbial growth and decay only, or it can be assumed to be diffusive, which means that microbial cells can move within the biofilm matrix also by diffusion. Detachment at the biofilm surface can be described by rates, which are properties of individual microbial species and are specified via the button “Particulate Variables.” Otherwise, it can be described by a global velocity, which means that all species are detached at the same rate.

The option “Variables” serves to activate or inactivate variables, which denote concentrations of substrates and microbial species. For each activated variable, AQUASIM automatically calculates mass balance equations for the substrates and microbial species in both the biofilm and the bulk fluid. The option “Processes” serves to activate or inactivate processes. Only activates processes are included in the calculation, while the value of the rates of inactivated processes is set to zero. This feature makes it possible to easily modify a model and to readily test alternative models. In AQUASIM, the term “Processes” refers to biotic or abiotic conversion reactions. There have to be specified by the user, while the equations describing transport processes are intrinsic parts of AQUASIM. The example shows the rate law and the stoichiometric coefficients of the process “heterotrophic growth.” The options “Initial Conditions” and “Input” serve to provide initial and influent values for the microbial species and substrates, as well as for the water flow rate.

The properties of the microbial species considered are specified via the button “Particulate Variables.” The density, defined as cell mass per unit cell volume, is the only properties that must be specified at all times. AQUASIM is set up such that additional features of the model are omitted if their parameters have a value of zero. These features include attachment of cells to the biofilm surface and to the solid matrix within the biofilm, individual detachment of cells from the biofilm surface or solid matrix, and cell diffusion in the pore water and in the solid matrix. Furthermore, the implementation of the model considers a liquid boundary layer at the biofilm surface that is omitted if the value of its resistance is set to zero. The button “Dissolved Variables” leads to a dialog box in which the properties of the dissolved substrates can be specified. The diffusivity of the substrate in the pore water of the biofilm must be specified, while the boundary layer resistance can be set to zero.

In general, a quantitative representation of the system studies is superior to a merely qualitative picture. This is one of the reasons for expressing hypotheses in mathematical form. A mathematical model consists of the full set of equations abstracting the information required to simulate a system. A rigorous and mechanistic representation of the real system must be based on the fundamental laws of physics, chemistry, and biology, which area also called first principles. The development of models based on first principles enforces systematic and imposes rational methods for approaching a problem. For example, biofilm models based on reaction and transport principles (Wanner et al., 1986) have proven to be useful not only for testing the soundness of different scientific concepts but also for establishing rational strategies for designing bioflm systems. Models based on first principles provide a unified view of microbial growth system, including biofilms. They promote lateral transfer of insight between various scientific domains. Diffusion-reaction models routinely used in chemical engineering are now widely used to simulate biofilm systems (Wanner et al., 1986 and 1996). Fluid mechanics methods have been used to study biofilm theology (Dockery et al., 2001; Stoodley et al., 1999) and hydrodynamic conditions in the liquid environment surrounding a biofilm matrix (Dillon et al, 2000 and 2001; Dupin et al., 2001; Eberl et al, 2001; Picioreanu et al., 2000a, 2000b and 2001). Laws of structural mechanics and finite element analysis method of civil engineering, have been used to study biofilm growth and detachment (Dupin et al., 2001; Picioreanu et al., 2001).

When a quantitative mathematical model of biofilm structure and function is constructed, it is advantageous to construct the model from submodels, each of which describes one of the various ongoing processes in a biofilm, including (1) biomass growth and decay, (2) biomass division and spreading, (3) substrate transport and reactions, (4) biomass detachment, (5) liquid flow past the biofilm, and (6) biomass attachment. Attachment is an important process because it determines the initial pattern of colonization of the substratum and the possible immigration of any type of cell from the liquid phase to various locations in the existing biofilm, The advantages of a modular biofilm model are manifold and include a better understanding of specific phenomena, better validation of individual model components, the possibility of exchanging routines or submodels with other biofilm models, more flexibility in solving decoupled model equations, and reflection of the modular structure of biofilm communities and processes.

For the processes described above, proper representation of biomass division and spreading is one of the most controversial topics. The difficulty in modeling the spreading of microbial cells inside colonies is that there must be a mechanism to release the pressure generated by the growing bacteria. Different solutions have been proposed, but given the lack of experimental evidence, none can claim to be correct. Current models for biofilm structure deal with bacteria in two different ways, depending mostly on the intended spatial scale of the model. One approach, individual-based modeling (IbM), attempts to model the biofilm community by describing the actions and properties of individual bacteria (Kreft et al., 1998 and 2001). IbM allows individual variability and treats bacterial cells as the fundamental entities. Essential state variables are, for example, the cell biomass (m), the cell volume (V) etc. The other approach treats biofilms as multiphase systems and uses volume averaging to develop macroscopic equations for biomass dynamics. These models can be called biomass-based models because they use the mass of cells per unit of volume (density or concentration [CX]) as the state variables for biomass. A comprehensive analysis of conditional tool has been performed (Wood et al., 1998 and 1999). The biomass-based models can be further divided into two classes on the basis of the mechanism used for biomass spreading. One subclass includes discrete biofilm models (i.e., cellular automata), in which biomass can be shifted only stepwise along a finite number of directions according to a set of discrete rules (Hermanowics et al., 1998 and 2001; Noguera et al., 1999; Picioreanu et al., 1998a and 1998b; Pizarro et al., 2001; Wimpenny et al., 1997). The other subclass of biomass-based models treats biomass as a continuum, and biomass spreading is generally modeled by differential equations widely used in physics (Dockery et al., 2001; Dupin et al., 2001; Eberl et al., 2001)

Figure 1.3Model biofilm system. (A) Continuously stirred tank rector containing a liquid phase in contact with a biofilm growing on a planar surface. (B) Rectanglar computational domain (2-D or 3-D) enclosing a small part of the whole biofilm. (C) Rectangular uniform grid used for solution of the partial differential equations for substrate diffusion and reaction. (D) Biofilm biomass contained in spherical particles holding one type of active biomass, as well as inactive biomass. Taken from Picioreanu et al. (2004)

Nitrogen compounds are essential for all living organisms since it is a necessary element of DNA, RNA and proteins. Although it is composed of 78% of the earth’s atmosphere as nitrogen gas, almost all bacteria except a few organisms cannot utilize this form of nitrogen directly. In many situations, fixed nitrogen is the limiting nutrient because its availability is usually much smaller than the potential uptake by, for example, plants (Pynaert, 2003). Hence, the supply of protein food for the global population by agriculture is recently dependent on the use of synthetic nitrogen fertilizer generated from atmospheric N2 by the Haber-Bosch process. The global estimation for biological nitrogen fixation is in the range of 200-240 Mt nitrogen, which indicates that the mass flows for nitrogen have a major impact on the global nitrogen cycle (Gijzen and Mulder, 2001).

The consumption of protein will yield the discharge of organic nitrogenous compounds in wastewater (Van Hulle, 2005). Some nitrogenous compounds derived from fertilizer accumulate and end up in wastewater in the form of ammonium or organic nitrogen. Other polluting nitrogenous compounds are nitrite and nitrate. Nitrate is originally used to make fertilizers, even though it is also used to make glass, explosives and so on. Nitrite is manufactured mainly for use as a food preservative. These nitrogenous compounds, i.e., organic nitrogen, ammonia, nitrite and nitrate, exist ubiquitously.

The discharge of these nitrogenous compounds into water environment results in several environmental and health problems. Essentially, ammonia is a nutrient for plants and it is responsible for eutrophication, i.e., undesirable and excessive growth of aquatic plants and algae. Such excessive growth of the aquatic vegetables would cause a depletion of oxygen since they consumes oxygen in the water, which has a significant impact on viability of fish. Additionally, the growth of the vegetables determines oxygen and pH of the surrounding water. The greater the growth of algae, the wider the fluctuation in levels of dissolved oxygen (DO) and pH will be. This affects metabolic processes in organisms seriously, leading to their death. Besides that, some blue-green algae have a potential to produce algal toxins, which fatally kill fish and livestock that drink the water (Antia et al., 1991). Ammonia itself is also toxic to water environmental organisms at concentration below 0.03 g-NH3-N/L (Solbe and Shurben, 1989). Nitrate pollution impeded the production of drinking water critically. Nitrite and nitrate in drinking water can result in oxygen shortage of newly born, which is alternatively called ‘blue baby syndrome’ (Knobeloch et al., 2001) and, during chlorination of drinking water, carcinogenic nitrosamines may be formed by the interaction of nitrite with compounds containing organic nitrogen. Therefore, nitrogenous compounds need to be removed from wastewater. For the removal of nitrogen, a wide variety of biological removal systems are available (Henze et al., 1995).

1.4.2 Biological nitrogen removal

Inorganic nitrogen, which comes from domestic and industrial wastewater, is normally found in most reduced form, ammonia. In wastewater treatment, nitrogen removal with microorganisms (bacteria) is most widely applied in wastewater treatment plant because biological nitrogen removal is less costly and less harmful to water environment than physicochemical counterpart. In the biological nitrogen removal, complete nitrogen removal is achieved by two successive processes: nitrification and denitrification.

Nitrification process

Nitrification is the aerobic oxidation of ammonia to nitrate (Rittmann and MaCarty, 2001). It is an essential process prior to the actual nitrogen removal by denitrification. The process consists of two sequential steps that are performed by tow phylogenetically unrelated groups of aerobic chemolithoautotrophic bacteria and, to a minor extent, some heterotrophic bacteria. In the first step, ammonia is oxidized to nitrite by ammonia-oxidizing bacterial (AOB) and, in the second step, nitrite-oxidizing bacteria (NOB). Sometimes AOB and NOB are summarized as nitrifiers. The stoichiometry for both reactions is given in equations 1.1 and 1.2, respectively (U.S. Environmental Protection Agency, 1975). In these cases, typical values for AOB and NOB biomass yield are used as follows:

Both groups of bacteria are chemolithoautotrophic and obligatory aerobic. Autotrophic means that they definitely fix and reduce inorganic carbon dioxide (CO2) for biosynthesis, which is an energy-expensive process. Such very unique characteristic of nitrifiers makes their yield values lower than that of aerobic heterotrophic bacteria. The fact that they utilize a nitrogen electron donor even lowers their cell yield due to less energy release per electron equivalent compared to organic electron donors. As a consequence, both AOB and NOB are considered slow growing bacteria. Molecular oxygen is utilized for endogenous respiration and conversion of reactant i.e., ammonia or nitrite. It is generally known that nitrifies grow well at slightly alkaline pH (7.2-8.2) and temperature between 25-35°C (Sharma and Ahlert, 1977). At a pH below 6.5, no growth of AOB is observed probably due to limited ammonia availability at such low pH value (Burton and Prosser, 2001). The optimal DO for AOB and NOB is normally 3-4 g-O2/m3 (Barnes and Bliss, 1983), although levels of 0.5 g-O2/m3 (Hanaki et al., 1990) and even 0.05 g-O2/m3 (Abeliovich, 1987) supported significant rates of ammonia oxidation but not nitrite oxidation (Bernet et al., 2001).

1.4.3 Phylogeny of nitrifying bacteria

Nitrifying bacteria (nitrifiers) have minimal nutrient requirements owing to their true chemolithotrophic nature. Nitrifiers are obligate aerobes, and they use oxygen for respiration and as a direct reactant for the initial monooxygenation of ammonia (NH4 +) to hydroxylamine (NH2OH). The most commonly known genus of bacteria that carries out ammonia oxidation is Nitrosomonas; however, Nitrosococcus, Nitrosopira, Nitrosovibrio, and Nitrosolobus are also able to oxidize ammonia to nitrite. The AOB, which all have the genus prefix Nitroso, are genetically diverse, but are related to each other in the β-subdivision of the proteobacteria (Teske et al., 1994). This diversity suggests that neither the Nitrosomonas genus nor any particular species within it (e.g., N. europaea) necessarily is dominant in a given system.

Although Nitrospira, Nitrospina, Nitrococcus, and Nitrocystis are recognized as NOB to sustain themselves from nitrite oxidation, Nitrobacter is the most famous genus of the NOB. Within the Nitrobacter genus, several subspecies are distinct, but closely related genetically within the α-subdivision of the proteobacteria (Teske et al. 1994). Recent findings using oligonucleotide probes targeted to the 16S rRNA of Nitrobacter, which indicates that Nitrobacter is not the most important nitrite-oxidizing genus in most wastewater treatment processes. Nitrospira more often is identified as the dominant NOB (Aoi et al., 2000). Since nitrifiers exist in water environment and wastewater treatment plants where organic compounds are present, such as in wastewater treatment plants, it might seem curious that they have not evolved to use organic molecules as their carbon source. While the biochemical reason that organic-carbon sources are excluded is not known, the persistence of their autotrophic dependence probably is related to their evolutionary link to photosynthetic microorganism (Teske et al., 1994).

1.4.4 Differential behavior of AOB and NOB

Several environment conditions affects the activity AOB and NOB. Generally, the amount of nitrate defines NOB activity under aerobic conditions. By setting optimal conditions, we can theoretically achieve not nitrite but ammonia oxidation since NOB are more sensitive to detrimental environmental conditions, e.g., unusual pH, low DO, temperature, solid retention time and so on, than AOB. Among the most important environmental parameters influencing ammonia and nitrite oxidation are the free ammonia (FA) and free nitrous acid (FNA) concentration, temperature, pH and DO concentration. Engineering challenge is how we can differentiate the activity of AOB with NOB critically.

FA and FNA inhibition of nitrifiers

The uncharged nitrogen forms are considered to be the actual substrate/inhibitor for ammonia and nitrite oxidation. The amount of FA and FNA can be calculated form temperature and pH using following equilibrium equations:

where Kb and Ka are ionization constants of ammonia and nitrous acid, respectively. The NH3 and HNO2 concentrations can be calculated from equations 1.5 - 1.8 proposed by Anthonisen et al. (1976):

(1.5 - 1.8 の式は省略）

where NH4 +-N and NO2 --N are ammonia- and nitrite-nitrogen concentrations, T is temperature in ºC, respectively. For these equilibriums 1.5 and 1.7, T and pH of the solution will determine the concentrations of FA and FNA. The toxicity effect of this FA and FNA on the two groups of nitrifiers has been described regarding a diagram proposed by Anthonisen et al. (1976). The diagram (Fig. 1.4), where the AOB are represented by Nitrosomonas and the NOB by Nitrobacter, indicates that inhibition of AOB by FA is likely in the rage of 10 to 150 g-N/m3 while NOB are likely inhibited at significant lower concentrations of 0.1 to 1 g-N/m3. In case of NOB, the key enzyme, a nitrite oxidoreductase (NOR), loses activity (Yang and Alleman, 1992). This difference in NH3 sensitivity could give rise to nitrite accumulation when wastewater with high ammonia concentration is treated. However, adaptation of NOB to high FA levels is observed by Turk and Mavinic (1989). They reported that NOB appeared capable of tolerating ever-increasing levels of FA concentrations up to 40 g NH3-N/m3. At low pH less than 7, FNA affect the activity of AOB and NOB. According to Figure 1.1, a FNA concentration of 0.2-2.8 g HNO3-N/m3 inhibits NOB.

Effect of oxygen

Both AOB and NOB require oxygen for their normal anabolism and catabolism. Low DO concentration will disrupt rates of ammonia and nitrite oxidation, leading to imbalance between the growth of AOB and NOB. The effect of DO on the specific growth rate of nitrifiers is generally governed by the Monod equation, where affinity constant of oxygen KO2 is a determining parameter. Considering the report that the constant for AOB and NOB are 0.6 and 2.2 g-O2/m3, respectively (Wiesmann, 1994), KO2 value for AOB are lower than that for NOB, indicating a higher oxygen affinity of AOB than that NOB at low DO concentrations. In such oxygen-limited systems, this feature could lead to a decrease in the amount of nitrite oxidation and therefore accumulation of nitrite (Bernet et al., 2001; Garrido et al., 1997; Pollice et al., 2002; Terada et al., 2004).

Besides the direct inhibitory effect of low DO, there is also an indirect effect. AOB exposed to low DO levels have been shown to generate higher amounts of the intermediate hydroxylamine, which might be the determinant compound of nitrite build-up (Yang and Alleman, 1992). Kindaichi et al. (2004) clarified that the addition of hydroxylamine decreases the activity of NOB, which alternatively lead to an increase of AOB activity and changes of microbial community in an autotrophic nitrifying biofilm.

Effect of temperature

Temperature is a key parameter in the nitrification process; however, the exact influence has not been clarified because of the interaction between mass transfer, chemical equilibrium and growth rate dependency. Normally, both AOB and NOB have similar temperature ranges for their activities. Both organisms have maximum growth rates at a temperature of 35ºC (Grunditz and Dalhammar, 2001); however, they prefer moderate temperature (20-30ºC). The activities significantly decrease at temperatures below 20ºC and above 40-45ºC because of enzyme disruptions. Generally, AOB grow faster than NOB at temperatures of more than 25ºC, whereas this is reversed at lower temperatures around 15ºC. The SHARON process (Single reactor High activity Ammonia Removal Over Nitrite) employs such principle. In this process, nitritation, oxidation of ammonia to nitrite, is established in chemostat by operating under high temperature conditions (above 25ºC) and maintaining an appropriate sludge retention time (SRT), which is also a selection pressure between AOB and NOB. Such selective operation keeps AOB in the reactor, while NOB are washed out and further nitratation, oxidation of nitrite to nitrate, can be prevented. Nitrite build-up would be very useful when treating low carbon/nitrogen-containing wastewater because subsequent denitrification requires less organic carbon in case via nitrite than in that via nitrate.

Furthermore, considering the influence of temperature on microbial community between AOB and NOB, increased temperature will increase the ratio of NH3/NH4 +, possibly causing inhibitory effects on the NOB. Additionally, an increase of temperature decrease saturated DO concentration, leading to oxygen-limited conditions disrupting the imbalance of AOB and NOB with possible nitrite accumulation.

Effect of pHI

In spite of a wide divergence of the reported effects of pH on nitrification, it is generally known that the optimum pH range for both AOB and NOB is from 7.2 to 8.2 (Pynaert, 2003). The range is also related to NH3/NH4 + and NO2 -/HNO2 ratios, where FA and FNA can exhibit inhibitory effects starting from certain pH. Ammonia oxidation brings acidifying conditions when it occurs (see equation 1.1). If buffer capacity of this environment is too low, the pH will decrease rapidly. Below pH 7, nitrification rate decrease even though there are some reports of nitrifying activity in acidic environments (Burton and Prosser, 2001; Tarre et al., 2004a, b).

Denitrification process

Denitrification is the dissimilatory reduction of nitrate or nitrite to mainly nitrogen gas. In other words, nitrate or nitrite is the electron acceptor used in energy generation. Denitrification normally occurs among heterotrophic and autotrophic bacteria, many of which can shift between oxygen respiration and nitrogen respiration. Denitrifying bacteria (denitrifiers) are common among the Gram-negative Proteobacteria, such as Pseudomonas, Alcaligenes, Paracoccus and Thiobacillus. Some Gram-positive bacteria, including Bacillus, can also denitrify. Even a few halophilic Archaea, such as Halobacterium, are able to denitrify. The denitrifiers used in environmental biotechnology are chemotrophs that can use organic or inorganic electron donors. Those that utilize organic electron donors are heterotrophs and are widespread among the Proteobacteria. Inorganic electron donors also can be used and gaining popularity (Rittmann and MaCarty, 2001). Hydrogen (H2) is an excellent electron donor for autotrophic denitrification. Its advantages include lower cost per electron equivalent compared to organic compounds, less biomass production than with heterotrophs, and absolutely no reduced nitrogen added. The main disadvantage of H2 in the past has been lack of a safe and efficient H2 transfer system. The recent development of membrane-dissolution devices overcomes the explosion hazard of conventional gas transfer and makes H2 a viable alternative (Lee and Rittmann, 2000, 2002). Reduced sulfur also can drive autotrophic denitrification. The most common source of reduced S is elemental sulfur, S(s), which is oxidized to SO4 2-. The S normally is embedded in a solid matrix that includes a solid base, such as CaCO3, because the oxidation of S(s) generates strong acid.

During biological heterotrophic denitrification, oxidized nitrogen forms are reduced and an organic electron donor is oxidized for energy conservation. This electron donor can be the organic material present in wastewater, or, in case of shortage, an externally added carbon source, e.g., acetate. An example of a denitrification reaction is given in equation 1.9, where nitrate is denitrified to nitrogen gas with acetate as an electron donor.

CH3COOH + 8/5 NO3 - + 4/5 H2O → 4/5 N2 + 2 H2CO3 + 8/5 OH- (1.9)

The pathways of denitrification are composed of four steps (equation 1.10). Each of the reduction steps is catalyzed by respective enzymes, i.e., nitrate reductase, nitrite reductase, nitric oxide reductase and nitrous oxide reductase.

NO3 - → NO2 - → NO (gas) → N2O (gas) → N2 (gas) (1.10)

NO and N2O are gaseous intermediates, which have to be avoided. Since the greenhouse effect of N2O is reported to be 300 times higher than that of CO2 (IPCC, 2001), emission of N2O should be reduced from wastewater (Tsuneda et al., 2005).

1.4.5 Application to novel nitrogen removal

Nitrogen removal via nitrite

As already mentioned in the previous chapters, nitrite is an intermediate in both nitrification and denitrification (Fig. 1.5). Accumulation or discharge of nitrite should be harmful to aqueous environment; hence, nitrite should be removed properly. Normally, ammonia is converted into nitrate by AOB and NOB under aerobic conditions; subsequently the nitrate is converted into nitrogen gas by denitrifiers under anoxic conditions. Such pathway via nitrate requires more oxygen for nitrification and organic carbon for denitrification than that via nitrite. Numerous environmental engineers have been focusing on biological nitrogen removal via not nitrate but nitrite because of economical advantages. Concretely, the nitrification-denitrification via nitrite saves around 25% on oxygen input for nitrification and 40% of organic carbon for denitrification (Abeling and Seyfried, 1992; Bernet et al., 1996; Eum and Choi, 2002; Oh and Silverstein, 1999; Turk and Mavinic, 1986). It also enables required hydraulic retention time (HRT) to decrease, which could achieve a small reactor volume.

Simultaneous nitrification and denitrification with biofilm

Nitrification and denitrification are complementary in many ways: (1) nitrification produces nitrite or nitrate that is a reactant in denitrification; (2) nitrification reduces the pH that is raised in denitrification; and (3) denitrification generates the alkalinity that is required in nitrification (Rittmann and MaCarty, 2001). Therefore, there exists obvious advantage to carry out simultaneous nitrification and denitrification in a single reactor. In that case, it is essential to make redox stratification, i.e. reaction sites for aerobic and anoxic part in a single reactor. In this thesis, the author is focusing on bacterial aggregated layer on surfaces, biofilm. The Biofilms have chemical gradients because of its thickness, leading to creation aerobic and anoxic part inside; therefore, they can theoretically provide such stratification. Engineering challenges are how we can create such redox stratification in biofilms and how we can make robust biofilm. Biofilm itself and its potential toward biofilm reactor will be described in the next chapter.

FISH is highly effective for detecting specific bacteria and analyzing the spatial distribution of a complex microbial community, due to the possibility of detecting specific bacterial cells at the single-cell level by in situ hybridization using phylogenetic markers (16S-rRNA-targeted oligonucleotide probes) labeled with a fluorescent compound (Amann et al., 1990). rRNA is an ideal target for in situ hybridization with oligonucleotide probes because: (i) it is present in all bacteria and the identification of natural populations is based on the phylogenetic classification of 16S rRNA sequences, (ii) a large number of sequences of different organisms are stored in databases, (iii) the high copy number per cell greatly increases detection sensitivity and enables the direct detection and observation of a single cell by using an epifluorescence microscope or a confocal scanning laser microscope (CSLM). FISH-dependent techniques have enabled the observation of the in situ microbial community structure in various types of biofilm communities, including those in natural environments and engineered systems. Generally, FISH is one of the most powerful tools and has become a reliable and commonly used method. Furthermore, the spatial organization of unknown and unculturable bacteria has been analyzed by the combined use of denaturing gradient gel electrophoresis (PCR-DGGE) which enables the design of an oligonucleotide probe for FISH following the determination of target bacterial species and their 16S rDNA sequences. Detailed schemes for analyzing complex microbial communities targeting specific but unknown and unculturable bacteria have been described by Amann et al. (1995).

1.5.2 Microsensors combined with FISH

Microsensors employing microelectrodes facilitate the measurement of the concentrations of substrates or products inside biofilms and are powerful tools for the estimation of the spatial distribution of in situ metabolic activity in biofilms. The principle of microsensor mostly relies on diffusion-dependent electrode reactions, scaling down the active surface area and diffusion distances lead to better signal stability, faster response, and practical independence of the microsensor signal on stirring of the external medium (Kühl and Revsbech, 2001). Microsensors for various chemical compounds such as N2O, NH4 +, NO2 -, NO3 -, O2, H2, H2S, and glucose and for pH have been developed and used to investigate chemical gradients in various types of biofilms on a micrometer scale. FISH has recently been combined successfully with microsensor measurements to investigate sulfate reduction (Ramsing et al., 1993), the nitrification in trickling filter biofilms (Schramm et al., 1996), and the nitrification in microbial aggregates (Schramm et al., 1998; 1999), fixed bed biofilms (Okabe et al., 1999), membrane-aerated biofilms (Hibiya et al., 2003; Schramm et al., 2000; Terada et al., 2003). The combination of the two methods provides reliable and direct information on the relationship between in situ microbial activity and the occurrence of specific microorganisms in a biofilm community (Schramm et al., 2003). Furthermore, the spatial distribution of metabolically active areas and active species in the biofilm can be simultaneously estimated.

1.5.3 In situ observation of nitrifying biofilms

Nitrifiers, AOB and NOB, are chemoautotrophs. Although nitrification is one of the most significant steps in biological nitrogen removal processes, this process is rate-limiting in both domestic and industrial wastewater treatment especially after some fluctuations of water quality and temperature. To accomplish high nitrification rate in the process, high concentrations of nitrifiers should be accumulated and retained for stable nitrification. Immobilization of nitrifiers is a quite important strategy to keep nitrification rate high. Effective methods for the immobilization of nitrifiers have been developed, such as the use of biofilms on supporting materials (Tsuneda et al., 2000), entrapment in polymer gels (Sumino et al., 1992), using fibrous net-works (Hayashi et al, 2002) and hollow-fiber membrane which is gas permeable (Semmens et al., 2003). Therefore, a better understanding of the spatial organization, and activities of immobilized nitrifying bacteria is necessary to improve removal performance and process stability. FISH dependent techniques provide reliable information regarding dominant species of nitrifying bacteria, their spatial distribution and activities in biofilms. Numerous researchers revealed that Nitrosomonas exists throughout the biofilm whereas location of Nitrospira sp. (NOB) is restricted to the inner part of the sewage wastewater biofilm as determined by combined analysis with a microelectrode (Okabe et al., 1999; Satoh et al., 2003; Schramm et al., 2000). Combination of a microelectrode with FISH has also made it possible to estimate the in situ cell-specific activity of uncultured nitrifying bacteria in the biofilm-like aggregate after the determination of the volumetric reaction rate calculated from microprofiles measured by microsensors and cell numbers of nitrifying bacteria measured by FISH (Schramm et al., 1999). Illustration for the analysis of the in situ organization of a biofilm community is shown in Fig. 1.6 (partly from Aoi, 2002). The combined information from various approaches would lead to the further clarification of the mechanism underlying treatment activities and highlight unfavorable fluctuations. Moreover, the information will be used to construct a novel and reliable mathematical model for the biofilm reaction based on the microscale activities and spatial organization of biofilm communities that have previously been regarded as a black-box (Aoi, 2002).

Identification and classification of cells in environmental and clinical samples can be critical for determining both prophylactic and curative responses. In dealing with contamination of food or water supplies by microorganisms, bacterial infections in tissues, and cancer-causing mutations in biopsy samples, a rapid diagnosis may be a life or death issue. Standard detection of cells in clinical or environmental samples requires any of a number of methods including culture, antigen detection, serology, nucleic acid amplification, and other biochemical assays [1, 2]. Despite the widespread use of these detection strategies, many can be time-consuming, difficult, and yield inconclusive results. As a result, a great deal of research in clinical and environmental chemistry focuses on developing rapid and specific methods to identify cells and gain information about cell type and characteristics. One of the most promising techniques for doing this is in situ detection of nucleic acids.

FISH probes several hundreds or thousands of nucleotides long complementary to nearly the entire 16S or 23S rRNA and containing multiple fluorophore labels have long been used to detect microorganisms in environmental samples because such lengthy probes offer reliable hybridization and intense signal. These probes are prepared enzymatically, typically by PCR [28] or in vitro transcription [29, 30], at which time multiple fluorophores are incorporated. Although polynucleotide probes allow the visualization of a significantly higher percentage of prokaryotes in a sample compared to singly labeled oligonucleotide probes [31], polynucleotide probes are only able to discriminate between distantly related groups, such as Bacteria, Crenarchaeota, and Euryarchaeota [32], because their sensitivity to small sequence differences is very low. Furthermore, long polynucleotide probes must be produced in the laboratory using costand labor-intensive protocols. Typical problems encountered include nonspecific binding of probe [32], high autofluorescence vs specific fluorescence [33], poor signal-to-background [34], low cellular detection compared to total cell count [35], and enzymatic degradation in situ [36].

More commonly, RNA-targeted FISH probes employ oligonucleotides 15–30 nucleotides long, with a DNA, PNA, or modified nucleic acid backbone, prepared synthetically (Fig. 1.1.A). Fluorescence is typically observed directly, using a fluorophore attached to the 5′-terminus, though in some cases 3′- or internally labeled probes are used. Common fluorophores used in RNA-targeted FISH diagnostics include fluorescein, tetramethylrhodamine (TAMRA), Texas Red, Cy3, and Cy5. Choice of dye is typically determined by its spectral properties and the availability of equipment for imaging. Labeled oligonucleotides are available from a variety of commercial sources, so it is typically not necessary for investigators to synthesize or purify probes.In some applications, indirect sensing is used instead of directly coupling the fluorophore to the probe. Indirect sensing strategies typically involve coupling an enzyme to the oligonucleotide probe, hybridizing to targets, then adding a fluorophore moiety that is recognized by and covalently binds to the enzyme [37]. These approaches can offer the significant advantage of brighter signals, but they tend to have low specificity [38, 39]. The most well-studied approach employs horseradish peroxidase (HRP)-labeled oligonucleotide probes [40-43]. HRP reacts with hydrogen peroxide and tyramide to produce a free radical on the tyramide, which covalently binds to a nearby tyrosine residue (Fig. 1.1.B) [44, 45]. A number of fluorophore-conjugated tyramides are available, thus allowing fluorescence detection of enzymatically deposited tyramide [45].

1.2.2. Standard Oligonucleotide FISH Protocols

Standard FISH protocols consist of four steps: (1) fixation and permeabilization of the sample, (2) hybridization of fluorescent probe, (3) washing away unbound probe, and (4) detection of labeled cells by microscopy or flow cytometry [32]. The first issue when setting up an RNA-targeted FISH experiment is getting the probes into the cells. Cells typically must be fixed so that high stringency washing steps may be performed to remove unbound probes. However, it is usually desirable to select fixatives that will disrupt cellular morphology as minimally as possible. The most common fixatives fall into two classes: cross-linking reagents, such as aldehydes, and precipitants such as methanol and ethanol [46]. Cross-linking reagents like formalin and paraformaldehyde are quite commonly used for permeabilization of gram-negative bacteria [47] and human cells [48], but may be ineffective in permeabilizing the cell walls of gram-positive bacteria [49]. Several possibilities exist to permeabilize gram-positive bacteria, and often different procedures are required for different species. Treatment of paraformaldehyde-fixed bacteria with cell wall-lytic enzymes, such as lysozyme or proteinase K, has been shown to increase the cellular permeability of Lactococci, Enterococci, and Streptococci [50]. Permeabilization by treatment with ethanol/formalin [51], high concentrations of ethanol or methanol [52], or heat [53], also has been successful in many cases.

Hybridization and washing conditions are highly dependent on the probe affinity and Tm and the cell type being examined, and optimal conditions must be determined empirically. Hybridization is performed at a few degrees lower than the probe Tm, typically in the 40–60°C range, in a buffer containing a relatively high salt concentration. For PNA FISH probes, higher hybridization temperatures can be used when the probes have higher Tm's [54]. The advantages to using higher hybridization temperatures are better disruption of target structures and better probe specificity. Washing is carried out in similar temperature ranges, often with the addition of higher concentrations of detergents such as SDS, Triton X, or Tween, or of formamide [32]. More stringent conditions may be required for PNA FISH probes. The washing step is typically difficult to optimize, but the most important in order to minimize false positives from unbound probes.

Insufficiently low signal, on the other hand, can be caused by a number of factors, including low ribosome or mRNA count, poor target accessibility, or impermeability of cells (see above). Low RNA content potentially can be circumvented by using strategies such as tyramide amplification, but this method requires conditions which tend to cause lysis of fixed cells [38, 55]. When target accessibility appears to be an issue, helper probes can be used or a different target site may need to be selected [56]. In some cases, addition of low concentrations of formamide to the hybridization buffer may improve the result, as formamide lowers the Tm of secondary structures (but also lowers the Tm of the probes) [46]. As a result, hybridizations in formamide-containing buffer must be run at lower temperatures.

After washing, cells may be analyzed by fluorescence microscopy or flow cytometry. Microscopy has the advantage of being rapid and simple, but an untrained eye can lead to incorrect reporting of data, and results are usually qualitative. Flow cytometry provides quantitative data on the fluorescence of individual cell populations, but instruments are quite expensive.

The greatest advantages of standard oligonucleotide probes for RNA-targeted diagnostics are that they are commercially available, relatively inexpensive, and well established in the literature for a plethora of applications. However, standard oligonucleotide probes do have several disadvantages. They are typically unable to distinguish related RNA sequences unless there are multiple nucleotide differences [32, 57]. In addition, careful handling is required to avoid nonspecific signals, especially during washing away of unbound probes [58]. The washing step increases the chances of error and nonspecific signal, and prevents application to live cells.

1.2.3. Molecular Beacons

Molecular beacons (MBs) were first developed in 1996 as tools for real-time PCR assays [59, 60]. They have since been developed for multiplex PCR assays [61, 62], solid-phase hybridization assays [63-65], biosensing [66], and FISH applications with both prokaryotic [67] and human cells [68]. Molecular beacons are oligonucleotides, typically with DNA, 2′-OMe RNA, or PNA backbones that have a stem-loop hairpin conformation in their native state, with a fluorophore and quencher at either end such that the probe is quenched while in the hairpin state [69, 70]. The loop region of the probe, or sometimes the loop and part of the stem [71], is complementary to an RNA target site, and upon binding, the hairpin is disrupted, separating the fluorophore and quencher, enhancing fluorescence signal (Fig. 1.2.A). The quenching efficiencies for several quenchers with many different fluorophores have been examined, allowing for the design of optimal fluorophore–quencher pairs [72].

A careful balance must be reached between the stem length and loop length to design optimal molecular beacons for mismatch discrimination. Solution experiments demonstrated that mismatch discrimination increases as the number of bases in the stem increases [73]. However, if the stem is too long, the kinetics of hybridization to target will be slow [74]. MBs with longer loop lengths tend to have lower hairpin Tm's, and thus increased kinetics of hybridization and decreased specificity, and MBs with very short stem lengths have lower signal-to-background ratios [74]. Design of MBs is substantially simplified by software, typically offered by companies that sell custom MBs.

MBs offer several advantages over standard oligonucleotide probes. Because MBs are quenched, no washing steps are required to remove unbound probe, and they may therefore be applicable to living cells assuming the cells can be permeabilized. Second, MBs have higher mismatch sensitivity than standard oligonucleotide FISH probes as a result of their conformational restraints [73]. The main sources of nonspecific signal in MBs are: (1) incomplete quenching, (2) hairpin–hairpin binding between two beacons, (3) nuclease degradation that separates the quencher and the fluorophore, and (4) nonspecific interactions with proteins and other small molecules within the cell that disrupt the hairpin structure [75]. The last may be the biggest problem in cellular diagnostics, since molecular beacons are known to interact with certain nucleic acid-binding proteins, disrupting the MB secondary structure and giving nonspecific signal [76].

Several new approaches to MBs recently have been developed to improve signal-to-background. Most notably, fluorescence resonance energy transfer (FRET) approaches have the potential to decrease background signal if the spectral overlap between the donor and acceptor is minimal [77]. This approach was first examined with the so-called “wavelength shifting molecular beacons,” in which an acceptor fluorophore, such as a rhodamine, was tethered to the fluorescein donor via a linker (Fig. 1.2.B) [78]. Wavelength shifting MBs were found to be useful in multiplex PCR assays, but have not been employed for FISH assays, possibly because the overlap between the donors and acceptors studied is too great to give a substantial signal-to-background advantage. A similar approach uses an acceptor fluorophore instead of a dark quencher, thereby changing the maximum emission wavelength between hairpin and bound states [79]. Going one step further, Bao and coworkers developed “dual FRET MBs,” in which two MBs bind side-by-side, with a donor fluorophore on one beacon thereby being brought into proximity with an acceptor fluorophore on the other beacon (Fig. 1.2.C) [80, 81]. Since both donor and acceptor fluorophores are quenched in their native state, and both need to hybridize adjacently in order for FRET to occur, background from nonspecific hairpin opening is substantially reduced.

1.2.4. Quenched Autoligation Probes

Quenched autoligation (QUAL) probes are a relatively new class of in situ hybridization probes that were developed for detection of sequences with high specificity [82-84]. Whereas molecular beacons rely on a conformational change to initiate fluorescence signal, QUAL probes utilize a chemical reaction [84]. QUAL probes consist of two oligonucleotide strands, the “dabsyl” probe and the phosphorothioate (thioate) probe (Fig. 1.3). The dabsyl probe is short, typically 7–10 nucleotides, while the thioate probe is longer, around 15–20 nucleotides. The dabsyl probe is modified such that it has a dabsyl group at its 5′-terminus, attached through an electrophilic sulfonate ester linkage, and a fluorescein or other fluorophore internally attached to a uridine base. Dabsyl is a dark quencher that efficiently quenches fluorescein, so the background fluorescence of the dabsyl probe is very low [85]. The thioate probe is modified such that it has a phosphorothioate group at its 3′-terminus. The two probes are designed such that they bind adjacently on their target, bringing the nucleophilic 3′-phosphorothioate close to the electrophilic 5′-dabsyl group. The phosphorothioate reacts to displace the dabsyl group, thereby ligating the two strands and unquenching the fluorophore (Fig. 1.3.A and B). It should be noted that both the 3′-terminal phosphorothioate in the nucleophilic probe and the bridging phosphorothioate linkage formed in the ligation product are quite stable to nucleases and hydrolysis [86]. The 5′-dabsyl group, however, is subject to slow hydrolysis in buffer, particularly in basic conditions or at high temperatures [87].

QUAL probes take advantage of the ability of very short oligonucleotides to sense single nucleotide mismatches while avoiding issues of redundancy and lack of affinity. The highest specificity is achieved when the mismatch is placed in the center of the short probe; substantially less discrimination is observed when the mismatch is at the end of the short probe or in the long probe [82]. Thus, specificity of QUAL probes is determined mostly by binding affinity as opposed to geometry of the reaction site, as mismatches in the center of short probe lead to the greatest Tm difference for binding to a matched vs mismatched target [82, 83]. Naturally, probing for sites with multiple mismatches will increase the specificity of QUAL probes.

When very short probes are used, there is a high probability for sequence redundancy. This issue is made moot in QUAL probes by requiring the two probes that bind adjacently. Thus, even if the short probe represents a sequence that has redundancy in the target RNA, no fluorescence is observed unless the longer probe binds adjacent to it. The lack of affinity of very short probes is dealt with by running the hybridization at relatively low temperatures (37°C or lower) and selecting sequences that have a Tm high enough to hybridize under the desired conditions. Since unbound probes are not fluorescent, no washing steps are required, and ligated fluorescent products are typically 20-mers or longer, which have high affinity for their target.

It should be noted, however, that it is not actually necessary that probes remain bound after ligation occurs in QUAL probes, as ligation permanently switches on fluorescent signal. In fact, if ligated probes dissociate from their target and new probes bind and ligate on the same template, signal amplification may occur [88]. Signal amplification by General Introduction 12 turnover is highly desirable for the detection of extremely low abundance targets such as mRNAs. A strategy of using “universal linkers” to attach the quencher to the 5′-terminus of the dabsyl probe yielded turnovers of nearly 100-fold on RNA templates in solution [88], and was used to detect mRNAs in human cells [89]. The product of the ligation reaction using the universal linker is destabilized compared to natural DNA, apparently because the alkane linker adds flexibility to the strand. This decreases product inhibition so that the target RNA can become a catalyst for generating multiple signals per target. Furthermore, the linker has been shown to destabilize the product without destabilizing the transition state of the reaction; in fact, the reaction rate is sped up by a factor of 4–5 [88].

In solution and solid-phase assays, QUAL probes were shown to accurately discriminate between all possible mismatches [84, 87]. QUAL probes offer several advantages over other RNA-targeted diagnostics strategies. Like MBs, unbound probes do not need to be washed out of cells, which reduces experimental time and decreases chances for error. QUAL probes may be less prone to nonspecific signals than MBs because turning on fluorescence requires a chemical reaction; thus, binding to proteins should not lead to nonspecific signals.

Disadvantages of QUAL probes include the slow hydrolysis of quencher leading to nonspecific fluorescence, the requirement that multiple probes be used, and the limited number of systems that they have been applied to thus far. The main sources of nonspecific signal in QUAL probes are: (1) incomplete quenching, (2) nuclease degradation in the 1–3 nucleotides between the fluorophore and quencher, and (3) hydrolysis of the quencher [75]. The last of these appears to be the greatest problem, requiring careful handling and storage of the probe.

Abe and Kool recently described a method to improve signal-to-background by using FRET–QUAL probes (Fig. 1.3.C) [89]. In these specialized QUAL probes, Cy5 was attached internally to the thioate probe. The ligation was monitored by excitation at 488 nm, which gives almost no excitation for Cy5. When the probes ligated, FRET between fluorescein and Cy5 allowed emission to be monitored at 665 nm, beyond the emission wavelength for fluorescein. Thus, background from incomplete quenching or nonspecific hydrolysis of the quencher was minimized, leading to substantially higher sensitivity.

QUAL probes are not yet commercially available, so they have not yet been adopted wide use in diagnostic settings.

Compared to other technologies, such as radioisotope labeling, MRI, ESR, and electrochemical detection, fluorescence imaging has many advantages, as it enables highly sensitive, non-invasive, and safe detection using readily available instruments. Another advantage of fluorescence imaging we should emphasize here is that the fluorescence signal of a molecule can be drastically modulated, so that sensors relying on ‘activation’, not just accumulation, can be utilized. Until the 1980s, however, fluorescence imaging was mainly applied to fixed samples owing to the lack of fluorescent chemosensors, or probes, suitable for imaging in living cells. In this review, ‘fluorescent probes’ are defined as molecules that react specifically with biological molecules to induce a concomitant change of their photochemical properties (fluorescence intensity, excitation/emission wavelength, and so forth). In the past two decades, following pioneering work by Tsien and co-workers on Ca2+ probes [90], there has been an explosive increase in the number of fluorescent probes developed [91, 92]. Today, several design strategies for fluorescent probes, including photoinduced electron transfer (PeT) [91, 93], fluorescence resonance energy transfer (FRET) [94], intramolecular charge transfer (ICT) [91 and 93], and spirocyclization [95], are well established and have been applied to many probes. Recent examples, developed in Nagano laboratory (University of Tokyo), are shown in Figure 1.4 [95-98]. In the 1990s, probes based on fluorescent proteins utilizing the FRET mechanism [94, 99] emerged with great success. More recently, probes based on nanoparticles [100, 101] and conjugated polymers [102] have been introduced, and some of them have already been applied to in vivo imaging [100]. Although we appreciate the significance of these relatively new scaffolds for fluorescent probes, the scope of this review is limited to recent advances of small-molecular probes in two selected categories owing to limitations of space. For more comprehensive information, readers should consult earlier reviews [91, 92, 103] or monographs [104, 105]. In addition, probes for other analytes, including reactive oxygen species (ROS) [106], reactive nitrogen species (RNS) [107], anions [108], and saccharides [109], have recently been reviewed elsewhere, as have probes with near-infrared emission [110].

1.3.1. Fluorescent probes for metal ions Ever since the advent of fluorescent probes, metal ions have been one of the most fruitful targets. The chemical structures of probes for metal ions can generally be divided into two moieties: chelator and fluorophore. The chelator moiety binds to the metals with a certain dissociation constant (Kd) and induces a change of the spectroscopic properties of the fluorophore. To obtain a large spectroscopic response, the structure of this moiety must be carefully selected, because the Kd value should lie between the concentrations of the monitored ion before and after the stimulus. It should be also noted that intracellular Kd is often different from the value in vitro [104]. Up to now, selective chelators for various metal ions, such as BAPTA for Ca2+, TPEN for Zn2+, and APTRA for Mg2+ (Fig. 1.5.A), have been developed and incorporated into fluorescent probes. The fluorophore is the moiety that determines the wavelengths of excitation and emission, as well as the mode of spectral change (turn on/off or ratiometric). For cell imaging, fluorophores excited by visible light (fluorescein, rhodamine, BODIPY, etc.) are desirable owing to their brightness and low phototoxicity, but those requiring UV excitation (benzofuran, etc.) are still used because they are suitable for ratiometric measurements. Ratiometric sensors, which exhibit spectral shift upon reaction or binding to the target, have advantages over turn-on/off sensors, because the results of ratiometric measurements are independent of dye concentration, bleaching, and illumination intensity [90]. Although dozens of probes have already been developed for the detection of Ca2+ [104, 111], Zn2+ [112], and others, improvements are still ongoing. For example, Bradley and co-workers immobilized a Ca2+ probe, Indo-1, on polystyrene beads to develop a cell-permeable microsphere-based sensor (Fig. 1.5.B), and succeeded in real-time calcium sensing in living cells [113]. At present, most of the probes for cations must be loaded into cells in the form of their acetoxymethyl (AM) ester [103], which is subsequently hydrolyzed by intracellular esterases, because the probes themselves often contain free acid moieties that impede cell-permeability. This AM strategy has proved to be powerful and versatile, but it has some disadvantages, such as incomplete hydrolysis, compartmentalization, and leakage over time [113]. The microsphere-based probe developed by Bradley et al. is cell-permeable in the form of acid salts, and reports calcium concentration at the surface of the beads. While further studies are necessary concerning the mechanism of cellular uptake and the control of intracellular localization of the microspheres, the combination of nano/micro-materials and organic fluorescent sensors is a promising approach. These days, growing attention is focused on two-photon microscopy (TPM) [114], which allows non-invasive imaging deep (up to 1 mm) within tissues, with high resolution. Although existing one-photon probes can be used for TPM, high laser power is often required to obtain clear images owing to the small two-photon action cross section (Φδ). To address this problem, Cho and co-workers developed two fluorescent probes, AMg1 [115] and ACa1 [116] (Fig. 1.5.C), which are specific to Mg2+ and Ca2+, respectively. Both probes contain 2-acetyl-6-(dimethylamino)naphthalene, which was shown previously by the authors to be an efficient polarity-sensitive two-photon fluorophore [117]. One-photon fluorescence of AMg1 and ACa1 exhibited a dramatic (>10 fold) increase upon addition of Mg2+ and Ca2+, respectively, presumably as a result of the blocking of PeT upon metal complexation. Two-photon excitation at 780 nm gave similar results, and notably, the probes had Φδ values over 100 GM, more than five fold greater than those of commercial probes (Mag-fura-2 and Oregon Green 488 BAPTA-1) [104]. Interestingly, when the probes were applied to cultured cells via the AM strategy, bright spots with blue-shifted emission were detected in the cells. On the basis of the solvent-dependency of the fluorophore, the authors attributed the spots to membrane-associated organelles, in which uncleaved AM esters accumulated. Hence, the blue-shifted emission was cut off to acquire the ‘true’ signal from the free and metal-bound probes localized in the cytosol. This simple manipulation allowed the precise distributions of the analytes inside the cells to be imaged. To demonstrate the utility of AMg-1, acute hippocampal slices from mice were incubated with the probe. The TPM images clearly revealed the Mg2+ distribution in the pyramidal neuron layer of the CA1 region. ACa-1 was similarly applied to rat hypothalamic slices and spontaneous Ca2+ waves were clearly visualized by TPM. The results, taken together, validate the applicability of these probes for two-photon imaging of the analytes in living tissue. Other recently developed metal ion probes include visible [97] or near-infrared [118] ratiometric fluorescent probes for Zn2+ reported by our group, and selective turn-on probes for Cu+ [119] and Hg2+ [120] developed in Chang’s laboratory.1.3.2. Fluorescent probes for proteolytic enzymeProteolytic enzymes, or proteases, are enzymes that catalyze hydrolysis of peptide bonds. They are said to occupy approximately 2% of the whole human genome, and are involved not only in many physiological events, but also in major diseases such as cancer, neurodegeneration, inflammation, and others [121]. Although several analytical methods have been established to investigate the biology of proteases [122], assays using fluorogenic substrates [123] are advantageous as they report not the mere expression, but rather the activity of the target enzymes. Another benefit of these substrate-based probes is that the signal is amplified by the catalytic enzyme reaction. Conventional fluorophores, however, are not optimal for applications using biological samples that have high levels of background signal (serum, urine, etc.). Lanthanide complexes with extraordinarily long-lived luminescence [124, 125] are expected to be advantageous for these applications, because the short-lived background fluorescence can be eliminated by time-resolved luminescence measurements. Recently, we have developed a novel long-lived protease probe by designing a method to modulate the PeT process within a lanthanide complex, and applied it to the diagnosis of cancer [126]. Another approach to elucidate the activities of specific enzymes in complex systems, termed activity-based protein profiling (ABPP), is currently attracting attention [127, 128]. In ABPP, samples are treated with small molecules known as activity-based probes (ABPs), which bind covalently to the active site of the target enzymes, and this is followed by analysis (imaging, SDS-PAGE, etc.) or purification of the target using tags (fluorescent molecules, biotin, etc.) attached to the ABPs. Compared with substrate-based Chapter 1 19 fluorescent probes, fluorescent ABPs have the advantage that they can provide direct biochemical evidence concerning the enzyme(s) responsible for the signal. Furthermore, the spatial distribution of the target can be precisely visualized with fluorescent ABPs. These features should be particularly advantageous in complicated systems containing various enzymes, such as living tissues. Hydrolytic enzymes, including proteases, are the most common targets of ABPP, which have been used in proteomic profiling of metalloproteases [129] and for the investigation of cysteine protease activity during tumor formation [130]. Although conventional ABPs are powerful tools with diverse applications, they are not suitable for live-cell or in vivo imaging owing to the lack of a fluorescence on/off switch. In other words, they are not ‘fluorescence probes’ in terms of the definition in this review, and extensive washout is necessary before images can be acquired. To overcome this limitation, Bogyo and co-workers developed ‘quenched’ probes (qABPs), which become fluorescent only after binding to the target [131]. The first qABP they developed (GB117) incorporated an acyloxymethyl ketone (AOMK) reactive moiety that targets cysteine proteases, together with BODIPY-TMR-X as a fluorophore, and QSY 7 as a quencher (Fig. 1.6.A). Before binding covalently to the enzyme, fluorescence of BODIPY-TMR-X was efficiently quenched (>70 fold) presumably via energy transfer to the quencher. When the probe binds to the active site of the enzyme, the quencher moiety is released from the probe, resulting in recovery of fluorescence. After confirming that qABP could successfully label cathepsins in vitro, the authors applied it to living cells. While non-specific staining of entire cells was observed with the unquenched ABP, a discrete labeling pattern that matched the distribution of lysosomes was obtained using GB117, without the need for a washing procedure. This result indicates that qABPs can provide a good signal-to-noise ratio, allowing direct imaging of protease activity in living cells. Interestingly, in an experiment using cell culture models, the probe and anti-cathepsin antibody gave different labeling patterns, illustrating the activity-directed nature of ABPs. Recently, an improved version of GB117, termed GB137, was reported by the same group [132]. GB137, with Cy 5 as a fluorophore, has excitation/emission in the NIR region, which is favorable for in vivo imaging (Fig. 1.6.B). GB137 was found to specifically label active cathepsins in a number of cancer cell lines, while it is insensitive to serum. After confirming in vivo that the probe with the AOMK ‘warhead’ was retained in cancer cells by covalently binding to cathepsins, the authors compared GB137 with the non-quenched counterpart (GB123). The non-quenched probe stained the whole body until unlabeled probe was completely eliminated from the animal. GB137, however, produced a specific and stable signal in tumors from the earliest time point, demonstrating the clear advantage of qABPs. The signal was reduced in mice treated with a cysteine protease inhibitor, K11777, suggesting the potential utility of the probe for the assessment of therapeutic agents that inhibit proteases. One possible disadvantage of ABPs over substrate-based approaches is the lack of signal amplification by the target enzymes. Although clear images with sufficient signal-to-noise ratios were acquired in the above cases, it remains yet to be seen whether this strategy is applicable to targets with lower expression levels. Another concern for activity-based imaging is that the inhibition of the target by ABPs might lead to undesirable and non-physiological biological responses. Furthermore, it is no less difficult to find specific activity-based inhibitors for the target enzyme, which are necessary to design ABPs, than to find substrates, which are required to develop substrate-based probes. Considering these points, it seems likely that ABPs and traditional probes will play complementary roles in the field of biological imaging.

Nitrification implies a chemolithoautotrophic oxidation of ammonia to nitrate under strict aerobic conditions and is conducted in two sequential oxidative stages: ammonia to nitrite (ammonia oxidation) and nitrite to nitrate (nitrite oxidation). Each stage is performed by different bacterial genera that use ammonia or nitrite as an energy source and molecular oxygen as an electron acceptor, while carbon dioxide is used as a carbon source. The most commonly recognized genus of bacteria that carries out ammonia oxidation is Nitrosomonas; however, Nitrosococcus, Nitrosopira, Nitrosovibrio and Nitrosolobus are also able to oxidize ammonium to nitrite. These ammonium oxidizers are genetically diverse, but related to each other in the beta subdivision of the Proteobacteria. In the nitrite oxidation stage, several genera such as Nitrospira, Nitrospina, Nitrococcus, and Nitrocystis are known to be involved. However, the most famous nitrite oxidizer genus is Nitrobacter, which is closely related genetically within the alpha subdivision of the Proteobacteria (Teske et al., 1994; Rittmann and McCarty, 2001). Equations for synthetic-oxidation using a representative measurement of yield and oxygen consumption for Nitrosomonas and Nitrobacter are as follows (U.S. Environmental Protection Agency, 1975).

In these equations, yields for Nitrosomonas and Nitrobacter are 0.15 mg cells/mg NH4-N oxidized and 0.02 mg cells/mg NO2-N oxidized, respectively. Oxygen consumption ratios in the equations are 3.16 mg O2/mg NH4-N oxidized and 1.11 mg O2/mg NO2-N oxidized, respectively. Also, it can be calculated that 7.07 mg alkalinity as CaCO3 is required per mg ammonia nitrogen oxidized. Severe pH depression can occur when the alkalinity in the wastewater approaches depletion by the acid produced in the nitrification process. The significance of pH depression in the nitrification process is that the reaction rates are rapidly depressed as the pH is reduced below 7.0. Therefore, in cases where the alkalinity of the wastewater will be depleted by the acid produced by nitrification, the proper alkalinity must be supplemented by a chemical addition, such as lime (U.S. Environmental Protection Agency, 1975). As the second step, denitrification is generally performed by a heterotrophic bioconversion process under anoxic conditions. The oxidized nitrogen compounds (NO2- and NO3-) are reduced to gaseous dinitrogen (N2) by heterotrophic microorganisms that use nitrite and/or nitrate instead of oxygen as electron acceptors and organic matter as carbon and energy source. Denitrifiers are common among the Gram-negative alpha and beta classes of the Proteobacteria, such as Pseudomonas, Alcaligenes, Paracoccus, and Thiobacillus. Some Gram-positive bacteria (such as Bacillus) and a few halophilic Archaea (such as Halobacterium) are also able to denitrify (Zumft, 1992). The process in environmental biotechnology is accomplished with a variety of electron donors and carbon sources such as: methanol, acetate, glucose, ethanol, and a few others (Table 1.1) (Ahn, 2006). Because methanol (CH3OH) was relatively inexpensive, it gained widespread use (Rittmann and McCarty, 2001). Combined dissimilation-synthesis equations for denitrification using methanol as an electron donor are as follows (U.S. Environmental Protection Agency, 1975):